Overview

Dataset statistics

Number of variables15
Number of observations15000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 MiB
Average record size in memory251.2 B

Variable types

Numeric9
DateTime1
Text1
Categorical4

Alerts

patient_id is uniformly distributedUniform
pain_level has 1387 (9.2%) zerosZeros

Reproduction

Analysis started2025-12-09 10:39:56.018860
Analysis finished2025-12-09 10:40:04.569095
Duration8.55 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

patient_id
Real number (ℝ)

Uniform 

Distinct500
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.5
Minimum1
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-12-09T10:40:04.654578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25.95
Q1125.75
median250.5
Q3375.25
95-th percentile475.05
Maximum500
Range499
Interquartile range (IQR)249.5

Descriptive statistics

Standard deviation144.34209
Coefficient of variation (CV)0.57621593
Kurtosis-1.2000096
Mean250.5
Median Absolute Deviation (MAD)125
Skewness0
Sum3757500
Variance20834.639
MonotonicityIncreasing
2025-12-09T10:40:05.474453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50030
 
0.2%
130
 
0.2%
230
 
0.2%
330
 
0.2%
430
 
0.2%
530
 
0.2%
630
 
0.2%
730
 
0.2%
830
 
0.2%
930
 
0.2%
Other values (490)14700
98.0%
ValueCountFrequency (%)
130
0.2%
230
0.2%
330
0.2%
430
0.2%
530
0.2%
630
0.2%
730
0.2%
830
0.2%
930
0.2%
1030
0.2%
ValueCountFrequency (%)
50030
0.2%
49930
0.2%
49830
0.2%
49730
0.2%
49630
0.2%
49530
0.2%
49430
0.2%
49330
0.2%
49230
0.2%
49130
0.2%
Distinct150
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size117.3 KiB
Minimum2024-03-01 06:00:00
Maximum2024-03-30 10:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-12-09T10:40:05.580357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:05.694577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

oxygen_saturation
Real number (ℝ)

Distinct110
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51496327
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-12-09T10:40:05.802742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.29357798
Q10.43119266
median0.51496327
Q30.60550459
95-th percentile0.74311927
Maximum1
Range1
Interquartile range (IQR)0.17431193

Descriptive statistics

Standard deviation0.13414318
Coefficient of variation (CV)0.26049076
Kurtosis0.10675207
Mean0.51496327
Median Absolute Deviation (MAD)0.083770612
Skewness-0.025487878
Sum7724.4491
Variance0.017994392
MonotonicityNot monotonic
2025-12-09T10:40:05.915344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5149632725763
 
5.1%
0.4862385321403
 
2.7%
0.504587156386
 
2.6%
0.4678899083384
 
2.6%
0.5596330275382
 
2.5%
0.4770642202382
 
2.5%
0.5137614679373
 
2.5%
0.5321100917370
 
2.5%
0.5504587156367
 
2.4%
0.495412844365
 
2.4%
Other values (100)10825
72.2%
ValueCountFrequency (%)
01
< 0.1%
0.0091743119271
< 0.1%
0.018348623851
< 0.1%
0.036697247711
< 0.1%
0.045871559631
< 0.1%
0.055045871562
< 0.1%
0.064220183491
< 0.1%
0.073394495412
< 0.1%
0.082568807342
< 0.1%
0.091743119272
< 0.1%
ValueCountFrequency (%)
11
 
< 0.1%
0.99082568811
 
< 0.1%
0.98165137612
< 0.1%
0.97247706422
< 0.1%
0.96330275232
< 0.1%
0.95412844042
< 0.1%
0.94495412841
 
< 0.1%
0.93577981651
 
< 0.1%
0.92660550464
< 0.1%
0.91743119271
 
< 0.1%

heart_rate
Real number (ℝ)

Distinct75
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49286447
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-12-09T10:40:06.026179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.27631579
Q10.40789474
median0.49286447
Q30.57894737
95-th percentile0.71052632
Maximum1
Range1
Interquartile range (IQR)0.17105263

Descriptive statistics

Standard deviation0.12762965
Coefficient of variation (CV)0.25895486
Kurtosis0.12179591
Mean0.49286447
Median Absolute Deviation (MAD)0.084969731
Skewness0.024408808
Sum7392.967
Variance0.016289327
MonotonicityNot monotonic
2025-12-09T10:40:06.147867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4928644682733
 
4.9%
0.4736842105586
 
3.9%
0.5131578947581
 
3.9%
0.4868421053562
 
3.7%
0.4473684211559
 
3.7%
0.5554
 
3.7%
0.5394736842549
 
3.7%
0.4605263158540
 
3.6%
0.4210526316519
 
3.5%
0.5526315789514
 
3.4%
Other values (65)9303
62.0%
ValueCountFrequency (%)
01
 
< 0.1%
0.039473684211
 
< 0.1%
0.052631578956
 
< 0.1%
0.065789473681
 
< 0.1%
0.078947368423
 
< 0.1%
0.092105263164
 
< 0.1%
0.10526315792
 
< 0.1%
0.11842105269
0.1%
0.131578947413
0.1%
0.144736842119
0.1%
ValueCountFrequency (%)
11
 
< 0.1%
0.98684210531
 
< 0.1%
0.96052631581
 
< 0.1%
0.94736842111
 
< 0.1%
0.93421052632
 
< 0.1%
0.92105263165
< 0.1%
0.90789473684
< 0.1%
0.89473684217
< 0.1%
0.88157894744
< 0.1%
0.86842105268
0.1%

temperature
Real number (ℝ)

Distinct33
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54571866
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-12-09T10:40:06.244132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.36363636
Q10.45454545
median0.54545455
Q30.63636364
95-th percentile0.75757576
Maximum1
Range1
Interquartile range (IQR)0.18181818

Descriptive statistics

Standard deviation0.11939559
Coefficient of variation (CV)0.21878598
Kurtosis0.18965134
Mean0.54571866
Median Absolute Deviation (MAD)0.090909091
Skewness-0.0067113468
Sum8185.7799
Variance0.014255307
MonotonicityNot monotonic
2025-12-09T10:40:06.336451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0.54545454551450
 
9.7%
0.57575757581361
 
9.1%
0.51515151521361
 
9.1%
0.48484848481271
 
8.5%
0.60606060611237
 
8.2%
0.45454545451037
 
6.9%
0.63636363641034
 
6.9%
0.4242424242854
 
5.7%
0.6666666667820
 
5.5%
0.5457186613773
 
5.2%
Other values (23)3802
25.3%
ValueCountFrequency (%)
01
 
< 0.1%
0.090909090913
 
< 0.1%
0.12121212123
 
< 0.1%
0.15151515155
 
< 0.1%
0.181818181828
 
0.2%
0.212121212131
 
0.2%
0.242424242459
 
0.4%
0.2727272727116
0.8%
0.303030303216
1.4%
0.3333333333286
1.9%
ValueCountFrequency (%)
12
 
< 0.1%
0.96969696977
 
< 0.1%
0.93939393945
 
< 0.1%
0.909090909110
 
0.1%
0.878787878843
 
0.3%
0.848484848564
 
0.4%
0.8181818182110
 
0.7%
0.7878787879219
1.5%
0.7575757576315
2.1%
0.7272727273462
3.1%

systolic_bp
Real number (ℝ)

Distinct75
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50590377
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-12-09T10:40:06.437427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q10.425
median0.50590377
Q30.5875
95-th percentile0.7125
Maximum1
Range1
Interquartile range (IQR)0.1625

Descriptive statistics

Standard deviation0.12172115
Coefficient of variation (CV)0.24060138
Kurtosis0.12239789
Mean0.50590377
Median Absolute Deviation (MAD)0.080903775
Skewness-0.010859657
Sum7588.5566
Variance0.014816038
MonotonicityNot monotonic
2025-12-09T10:40:06.547363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5059037748721
 
4.8%
0.5125616
 
4.1%
0.4875577
 
3.8%
0.525577
 
3.8%
0.5557
 
3.7%
0.5375543
 
3.6%
0.475542
 
3.6%
0.55520
 
3.5%
0.5875508
 
3.4%
0.45503
 
3.4%
Other values (65)9336
62.2%
ValueCountFrequency (%)
01
 
< 0.1%
0.052
 
< 0.1%
0.06251
 
< 0.1%
0.0753
 
< 0.1%
0.08752
 
< 0.1%
0.14
< 0.1%
0.11254
< 0.1%
0.1254
< 0.1%
0.13755
< 0.1%
0.158
0.1%
ValueCountFrequency (%)
11
 
< 0.1%
0.96252
 
< 0.1%
0.951
 
< 0.1%
0.91253
 
< 0.1%
0.91
 
< 0.1%
0.88753
 
< 0.1%
0.87511
0.1%
0.862517
0.1%
0.859
0.1%
0.837518
0.1%

diastolic_bp
Real number (ℝ)

Distinct54
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51692482
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-12-09T10:40:06.654516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.30909091
Q10.43636364
median0.51692482
Q30.6
95-th percentile0.72727273
Maximum1
Range1
Interquartile range (IQR)0.16363636

Descriptive statistics

Standard deviation0.1237728
Coefficient of variation (CV)0.23944062
Kurtosis0.15441664
Mean0.51692482
Median Absolute Deviation (MAD)0.080561186
Skewness-0.015660858
Sum7753.8723
Variance0.015319706
MonotonicityNot monotonic
2025-12-09T10:40:06.773842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4909090909814
 
5.4%
0.5090909091812
 
5.4%
0.5454545455797
 
5.3%
0.5272727273792
 
5.3%
0.4727272727768
 
5.1%
0.5636363636740
 
4.9%
0.5169248225738
 
4.9%
0.5818181818729
 
4.9%
0.4545454545721
 
4.8%
0.6657
 
4.4%
Other values (44)7432
49.5%
ValueCountFrequency (%)
01
 
< 0.1%
0.072727272735
 
< 0.1%
0.090909090913
 
< 0.1%
0.10909090913
 
< 0.1%
0.12727272738
 
0.1%
0.145454545510
 
0.1%
0.163636363619
 
0.1%
0.181818181822
 
0.1%
0.239
0.3%
0.218181818255
0.4%
ValueCountFrequency (%)
11
 
< 0.1%
0.98181818182
 
< 0.1%
0.96363636363
 
< 0.1%
0.94545454551
 
< 0.1%
0.92727272736
 
< 0.1%
0.90909090912
 
< 0.1%
0.89090909098
 
0.1%
0.872727272724
0.2%
0.854545454530
0.2%
0.836363636431
0.2%

weight
Real number (ℝ)

Distinct863
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52305758
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-12-09T10:40:06.884283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.30505415
Q10.4368231
median0.52305758
Q30.60830325
95-th percentile0.74187726
Maximum1
Range1
Interquartile range (IQR)0.17148014

Descriptive statistics

Standard deviation0.13147569
Coefficient of variation (CV)0.25135989
Kurtosis0.15749355
Mean0.52305758
Median Absolute Deviation (MAD)0.085331944
Skewness-0.008635884
Sum7845.8636
Variance0.017285858
MonotonicityNot monotonic
2025-12-09T10:40:06.999888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5230575762741
 
4.9%
0.588447653454
 
0.4%
0.572202166151
 
0.3%
0.560469314150
 
0.3%
0.549
 
0.3%
0.487364620948
 
0.3%
0.53519855648
 
0.3%
0.549638989247
 
0.3%
0.590252707647
 
0.3%
0.496389891746
 
0.3%
Other values (853)13819
92.1%
ValueCountFrequency (%)
01
< 0.1%
0.0036101083031
< 0.1%
0.018953068591
< 0.1%
0.033393501812
< 0.1%
0.048736462091
< 0.1%
0.055054151621
< 0.1%
0.057761732851
< 0.1%
0.060469314081
< 0.1%
0.061371841161
< 0.1%
0.065884476531
< 0.1%
ValueCountFrequency (%)
11
< 0.1%
0.99638989171
< 0.1%
0.99277978341
< 0.1%
0.97472924191
< 0.1%
0.97202166061
< 0.1%
0.96931407941
< 0.1%
0.96119133571
< 0.1%
0.95036101081
< 0.1%
0.94584837551
< 0.1%
0.94494584841
< 0.1%

blood_glucose
Real number (ℝ)

Distinct138
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54387104
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-12-09T10:40:07.120908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.34161491
Q10.46583851
median0.54387104
Q30.62111801
95-th percentile0.74534161
Maximum1
Range1
Interquartile range (IQR)0.1552795

Descriptive statistics

Standard deviation0.1202551
Coefficient of variation (CV)0.22110958
Kurtosis0.14338972
Mean0.54387104
Median Absolute Deviation (MAD)0.078032528
Skewness-0.055816246
Sum8158.0656
Variance0.014461288
MonotonicityNot monotonic
2025-12-09T10:40:07.265918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5438710369752
 
5.0%
0.5652173913305
 
2.0%
0.5590062112297
 
2.0%
0.5155279503296
 
2.0%
0.5527950311295
 
2.0%
0.5465838509292
 
1.9%
0.5714285714286
 
1.9%
0.5838509317285
 
1.9%
0.5403726708281
 
1.9%
0.5279503106279
 
1.9%
Other values (128)11632
77.5%
ValueCountFrequency (%)
01
 
< 0.1%
0.086956521742
 
< 0.1%
0.099378881991
 
< 0.1%
0.11180124222
 
< 0.1%
0.12422360252
 
< 0.1%
0.13043478263
< 0.1%
0.13664596271
 
< 0.1%
0.14285714291
 
< 0.1%
0.1490683233
< 0.1%
0.15527950316
< 0.1%
ValueCountFrequency (%)
11
 
< 0.1%
0.99378881991
 
< 0.1%
0.93788819881
 
< 0.1%
0.92546583852
 
< 0.1%
0.91925465842
 
< 0.1%
0.91304347831
 
< 0.1%
0.90683229811
 
< 0.1%
0.9006211185
< 0.1%
0.89440993797
< 0.1%
0.88819875789
0.1%

pain_level
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9926
Minimum0
Maximum10
Zeros1387
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size117.3 KiB
2025-12-09T10:40:07.349135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.1707857
Coefficient of variation (CV)0.63509709
Kurtosis-1.2229191
Mean4.9926
Median Absolute Deviation (MAD)3
Skewness0.00335657
Sum74889
Variance10.053882
MonotonicityNot monotonic
2025-12-09T10:40:07.423834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
01387
9.2%
101383
9.2%
71380
9.2%
41379
9.2%
11377
9.2%
51371
9.1%
31364
9.1%
81357
9.0%
91349
9.0%
21339
8.9%
ValueCountFrequency (%)
01387
9.2%
11377
9.2%
21339
8.9%
31364
9.1%
41379
9.2%
51371
9.1%
61314
8.8%
71380
9.2%
81357
9.0%
91349
9.0%
ValueCountFrequency (%)
101383
9.2%
91349
9.0%
81357
9.0%
71380
9.2%
61314
8.8%
51371
9.1%
41379
9.2%
31364
9.1%
21339
8.9%
11377
9.2%
Distinct61
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2025-12-09T10:40:07.580266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length57
Median length55
Mean length41.2404
Min length31

Characters and Unicode

Total characters618606
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPatient reports feeling severe with a pain level of 8.
2nd rowComplains of severe fatigue and no nausea.
3rd rowNoted elevated stress. Sleep was good.
4th rowNo major complaints. Sleep quality: Excellent.
5th rowNo major complaints. Sleep quality: Excellent.
ValueCountFrequency (%)
sleep6029
 
6.1%
of5985
 
6.0%
feeling5969
 
6.0%
patient5969
 
6.0%
no5430
 
5.5%
elevated3015
 
3.0%
noted3015
 
3.0%
stress3015
 
3.0%
was3015
 
3.0%
quality3014
 
3.0%
Other values (33)54506
55.1%
2025-12-09T10:40:07.829688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
83962
13.6%
e78442
12.7%
a48011
 
7.8%
l46689
 
7.5%
t38329
 
6.2%
n36961
 
6.0%
o35486
 
5.7%
i34174
 
5.5%
s25283
 
4.1%
.21029
 
3.4%
Other values (33)170240
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)618606
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
83962
13.6%
e78442
12.7%
a48011
 
7.8%
l46689
 
7.5%
t38329
 
6.2%
n36961
 
6.0%
o35486
 
5.7%
i34174
 
5.5%
s25283
 
4.1%
.21029
 
3.4%
Other values (33)170240
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)618606
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
83962
13.6%
e78442
12.7%
a48011
 
7.8%
l46689
 
7.5%
t38329
 
6.2%
n36961
 
6.0%
o35486
 
5.7%
i34174
 
5.5%
s25283
 
4.1%
.21029
 
3.4%
Other values (33)170240
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)618606
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
83962
13.6%
e78442
12.7%
a48011
 
7.8%
l46689
 
7.5%
t38329
 
6.2%
n36961
 
6.0%
o35486
 
5.7%
i34174
 
5.5%
s25283
 
4.1%
.21029
 
3.4%
Other values (33)170240
27.5%

nausea_encoded
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size732.6 KiB
0
12019 
1
2981 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
012019
80.1%
12981
 
19.9%

Length

2025-12-09T10:40:07.928437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-09T10:40:07.991095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
012019
80.1%
12981
 
19.9%

Most occurring characters

ValueCountFrequency (%)
012019
80.1%
12981
 
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)15000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
012019
80.1%
12981
 
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
012019
80.1%
12981
 
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
012019
80.1%
12981
 
19.9%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size732.6 KiB
0
6064 
3
5935 
1
3000 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row3
2nd row3
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
06064
40.4%
35935
39.6%
13000
20.0%
21
 
< 0.1%

Length

2025-12-09T10:40:08.063447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-09T10:40:08.130421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
06064
40.4%
35935
39.6%
13000
20.0%
21
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
06064
40.4%
35935
39.6%
13000
20.0%
21
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)15000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
06064
40.4%
35935
39.6%
13000
20.0%
21
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
06064
40.4%
35935
39.6%
13000
20.0%
21
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
06064
40.4%
35935
39.6%
13000
20.0%
21
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size732.6 KiB
2
6133 
1
4434 
0
2911 
3
1522 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters15000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
26133
40.9%
14434
29.6%
02911
19.4%
31522
 
10.1%

Length

2025-12-09T10:40:08.226799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-09T10:40:08.295978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
26133
40.9%
14434
29.6%
02911
19.4%
31522
 
10.1%

Most occurring characters

ValueCountFrequency (%)
26133
40.9%
14434
29.6%
02911
19.4%
31522
 
10.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)15000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
26133
40.9%
14434
29.6%
02911
19.4%
31522
 
10.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
26133
40.9%
14434
29.6%
02911
19.4%
31522
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
26133
40.9%
14434
29.6%
02911
19.4%
31522
 
10.1%

clinical_sentiment
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size835.1 KiB
POSITIVE
8403 
NEGATIVE
6597 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters120000
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNEGATIVE
2nd rowNEGATIVE
3rd rowPOSITIVE
4th rowPOSITIVE
5th rowPOSITIVE

Common Values

ValueCountFrequency (%)
POSITIVE8403
56.0%
NEGATIVE6597
44.0%

Length

2025-12-09T10:40:08.384413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-09T10:40:08.443043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
positive8403
56.0%
negative6597
44.0%

Most occurring characters

ValueCountFrequency (%)
I23403
19.5%
E21597
18.0%
V15000
12.5%
T15000
12.5%
O8403
 
7.0%
P8403
 
7.0%
S8403
 
7.0%
N6597
 
5.5%
G6597
 
5.5%
A6597
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)120000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I23403
19.5%
E21597
18.0%
V15000
12.5%
T15000
12.5%
O8403
 
7.0%
P8403
 
7.0%
S8403
 
7.0%
N6597
 
5.5%
G6597
 
5.5%
A6597
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)120000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I23403
19.5%
E21597
18.0%
V15000
12.5%
T15000
12.5%
O8403
 
7.0%
P8403
 
7.0%
S8403
 
7.0%
N6597
 
5.5%
G6597
 
5.5%
A6597
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)120000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I23403
19.5%
E21597
18.0%
V15000
12.5%
T15000
12.5%
O8403
 
7.0%
P8403
 
7.0%
S8403
 
7.0%
N6597
 
5.5%
G6597
 
5.5%
A6597
 
5.5%

Interactions

2025-12-09T10:40:03.544336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:56.753287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:57.631469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:58.720475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:59.780271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:00.535725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:01.304905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:02.057832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:02.790971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:03.623286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:56.848723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:57.752162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:58.838717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:59.862640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:00.618560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:01.390281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:02.142131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:02.881734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:03.709022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:56.930109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:57.885255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:58.981701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:59.956466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:00.703111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:01.473162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:02.220472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:02.965549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:03.788831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:57.011084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:58.005257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:59.101000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:00.039586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:00.785535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:01.552687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:02.300900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:03.059835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:03.869822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:57.097075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:58.123760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:59.230245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:00.121735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:00.869535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:01.632965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:02.381985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:03.143901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:03.954552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:57.178280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:58.244614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:59.361934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:00.204783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:00.957665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:01.717625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:02.461503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:03.223983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:04.035569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:57.260890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:58.370572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:59.496318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:00.288020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:01.053021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:01.798300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:02.540712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:03.305310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:04.129156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:57.385736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:58.483695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:59.611713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:00.369131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:01.140027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:01.878762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:02.622414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:03.384303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:04.209190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:57.510274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:58.598620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:39:59.693661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:00.454395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:01.221500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:01.959952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:02.704745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-09T10:40:03.463912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-09T10:40:08.500176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
blood_glucoseclinical_sentimentdiastolic_bpfatigue_level_encodedheart_ratenausea_encodedoxygen_saturationpain_levelpatient_idsleep_quality_encodedsystolic_bptemperatureweight
blood_glucose1.0000.000-0.0190.000-0.0080.0270.0020.0050.0090.000-0.006-0.017-0.006
clinical_sentiment0.0001.0000.0000.0000.0100.0000.0000.0100.0260.2450.0100.0000.014
diastolic_bp-0.0190.0001.0000.0000.0040.015-0.003-0.0150.0130.0000.011-0.0060.000
fatigue_level_encoded0.0000.0000.0001.0000.0060.0000.0000.0030.0000.0030.0000.0000.000
heart_rate-0.0080.0100.0040.0061.0000.0200.009-0.007-0.0010.0000.005-0.0030.003
nausea_encoded0.0270.0000.0150.0000.0201.0000.0140.0000.0150.0000.0330.0140.017
oxygen_saturation0.0020.000-0.0030.0000.0090.0141.0000.010-0.0150.000-0.0070.0020.006
pain_level0.0050.010-0.0150.003-0.0070.0000.0101.000-0.0010.000-0.0010.0000.023
patient_id0.0090.0260.0130.000-0.0010.015-0.015-0.0011.0000.0000.010-0.0170.008
sleep_quality_encoded0.0000.2450.0000.0030.0000.0000.0000.0000.0001.0000.0000.0000.000
systolic_bp-0.0060.0100.0110.0000.0050.033-0.007-0.0010.0100.0001.0000.001-0.010
temperature-0.0170.000-0.0060.000-0.0030.0140.0020.000-0.0170.0000.0011.0000.002
weight-0.0060.0140.0000.0000.0030.0170.0060.0230.0080.000-0.0100.0021.000

Missing values

2025-12-09T10:40:04.343535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-09T10:40:04.476072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

patient_idtimestampoxygen_saturationheart_ratetemperaturesystolic_bpdiastolic_bpweightblood_glucosepain_levelclinical_notenausea_encodedfatigue_level_encodedsleep_quality_encodedclinical_sentiment
012024-03-01 08:00:000.7064220.6842110.7878790.37500.7636360.3140790.6645968Patient reports feeling severe with a pain level of 8.031NEGATIVE
112024-03-02 08:00:000.4403670.7105260.6666670.53750.5272730.6534300.5465849Complains of severe fatigue and no nausea.032NEGATIVE
212024-03-03 08:00:000.7981650.4342110.6060610.65000.3818180.6173290.6149073Noted elevated stress. Sleep was good.102POSITIVE
312024-03-04 09:00:000.5412840.2236840.6060610.32500.6909090.5230580.74534210No major complaints. Sleep quality: Excellent.010POSITIVE
412024-03-05 09:00:000.3944950.3684210.4848480.41250.6181820.4512640.5652177No major complaints. Sleep quality: Excellent.010POSITIVE
512024-03-06 06:00:000.4220180.5921050.7575760.38750.4909090.4837550.3478260Patient feeling generally well.030POSITIVE
612024-03-07 08:00:000.3944950.3157890.5757580.60000.3090910.4945850.54658410Patient reports feeling severe with a pain level of 10.032NEGATIVE
712024-03-08 08:00:000.5779820.5394740.5457190.53750.4000000.5108300.62111810No major complaints. Sleep quality: Good.032POSITIVE
812024-03-09 09:00:000.4220180.5657890.5151520.51250.4363640.6010830.4968944Complains of severe fatigue and no nausea.032NEGATIVE
912024-03-10 07:00:000.4678900.4078950.6363640.58750.5169250.5622740.5590064Complains of mild fatigue and no nausea.000NEGATIVE
patient_idtimestampoxygen_saturationheart_ratetemperaturesystolic_bpdiastolic_bpweightblood_glucosepain_levelclinical_notenausea_encodedfatigue_level_encodedsleep_quality_encodedclinical_sentiment
149905002024-03-21 08:00:000.4678900.4736840.5757580.38750.5169250.5230580.4099380Complains of mild fatigue and no nausea.002NEGATIVE
149915002024-03-22 09:00:000.5688070.5789470.6363640.56250.6909090.4323100.4409940Patient reports feeling none with a pain level of 0.032NEGATIVE
149925002024-03-23 09:00:000.5779820.4342110.4545450.51250.4181820.5469310.6832301Patient feeling generally well.032POSITIVE
149935002024-03-24 06:00:000.4495410.5921050.5454550.62500.7090910.5487360.5652176Patient feeling generally well.010POSITIVE
149945002024-03-25 08:00:000.5321100.5921050.5151520.83750.5090910.5451260.5652177Complains of mild fatigue and no nausea.001NEGATIVE
149955002024-03-26 07:00:000.3302750.3421050.6060610.42500.5169250.2274370.7515535No major complaints. Sleep quality: Good.102POSITIVE
149965002024-03-27 10:00:000.6972480.3815790.7575760.57500.3636360.5234660.4223600Complains of moderate fatigue and no nausea.010NEGATIVE
149975002024-03-28 08:00:000.4403670.3157890.7878790.38750.5454550.4648010.5031064Patient reports feeling none with a pain level of 4.031NEGATIVE
149985002024-03-29 09:00:000.4770640.4928640.4242420.36250.5272730.3375450.5279505Noted elevated stress. Sleep was good.032POSITIVE
149995002024-03-30 08:00:000.4036700.2368420.6060610.58750.2727270.4025270.5031060Noted elevated stress. Sleep was poor.033NEGATIVE